is popular more likeable? choice status by intrinsic

Is Popular More Likeable?Choice Status by IntrinsicAppeal in an ExperimentalMusic Market

Freda B. Lynn1, Mark H. Walker2,and Colin Peterson3

Abstract

There is widespread agreement from many areas of status research that evaluators’ judg-ments of performances can be distorted by the status of the performer. The question arisesas to whether status distorts perceptions differently at different levels of performance quality.Using data from the Columbia Musiclab study, we conduct a large-scale test of whether theeffect of popularity on private perceptions of likeability is contingent on songs’ intrinsicappeal. We discover that choice status (i.e., popularity) can boost perceptions of a song’s like-ability but only for songs of lower quality. In effect, the likeability halo created by popularity isone mechanism for why it is that ‘‘bad’’ songs can sometimes become more successful thansongs that are intrinsically more appealing. But this same mechanism does not explainwhy ‘‘good’’ songs sometimes turn into superstars. This study suggests that status theoriesbe refined to consider heterogeneous effects.

Keywords

popularity, social influence, status and quality, cultural markets, choice status

To study status advantage is really to

study the interplay between status and

quality. Often discussed as the Matthew

Effect (DiPrete and Eirich 2006; Merton

1968), a core theme is that high statusactors benefit because they ‘‘get more for

the same.’’ Experimental research, for

example, clearly documents that evalua-

tions of an actor’s quality changes in the

presence of certain status markers, such

as motherhood (Correll, Benard, and

Paik 2007), gender (Moss-Racusin et al.

2012), and sociometric status (Lynn etal. 2016). In broad strokes, this behavior

is posited to occur because evaluators’

perceptions of a candidate are affected

not only by actual performance quality

but also by the performer’s status position

(Berger et al. 1977).

A central question that arises then is

the form of this weighting process:

how much does status matter relative to

1University of Iowa, Iowa City, USA2Louisiana State University, Baton Rouge, LA, USA3Stanford University, Stanford, CA, USA

Corresponding Author:Freda B. Lynn, University of Iowa, W143 Seashore

Hall, Iowa City, Iowa 52241, USA.Email: [email protected]

Social Psychology Quarterly2016, Vol. 79(2) 168–180

� American Sociological Association 2016DOI: 10.1177/0190272516645603

http://spq.sagepub.com

at The University of Iowa Libraries on June 10, 2016spq.sagepub.comDownloaded from

http://spq.sagepub.com/

quality? In the present study, we focus on

the fundamental and yet relatively

ignored issue of whether the effect of sta-

tus is contingent on quality (see Foschi,

Sigerson, and Lembesis 1995). For exam-

ple, in the creation of the expectation

states paradigm, Berger et al. (1977) laid

out a formal procedure for how different

task-relevant and status characteristics

would combine to form performance

expectations, where the form of aggrega-

tion was assumed to be consistent across

all levels of task proficiency (see Foschi

et al. 1995:336–37). More recently, Gould

(2002) developed a generalized model of

the Matthew Effect in which i’s percep-

tion of j’s quality—a precursor to whether

i decides to ‘‘attach’’ to j in the future (e.g.,

adopt, hire, purchase)—is calculated as

a weighted combination of j’s ‘‘true’’ qual-

ity and j’s status, which he operational-

ized with respect to the extent to which j

is already sought after or deferred to by

others in the market (for similar theories,see Becker 1991; Cialdini 1984; Hedstrom

1998; Podolny 1993). But neither Gould

(2002) nor those building from his model

of hierarchy formation (Lynn, Podolny,

and Tao 2009; Manzo and Baldassarri

2015) explored whether the weight given

to status might differ at high and low lev-

els of quality. If there is heterogeneity inhow status can distort perceptions of

quality (by level of actual quality) and

yet we assume that there is not, this over-

sight diminishes our predictive capabil-

ity, and we ignore an opportunity to refine

our theoretical understanding of why sta-

tus information matters to decision

makers.The extant literature suggests that sta-

tus markers are more likely to shape how

evaluators perceive low or average qual-

ity performers compared to top perform-

ers. For example, with respect to the

effect of gender on applicant evaluation,

Foschi et al. (1995) report a reverse dou-

ble standard that favors the female

applicant when the applicant is ‘‘excel-

lent’’; however, female applicants are typ-

ically disadvantaged when candidates are

average performers (Foschi and Valen-

zuela 2012; Heilman, Martell, and Simon

1988; but see Benard and Correll 2010).

Similarly, with respect to choice status,

Lynn et al. (2016) suggest that lower-

skilled candidates can benefit more from

being presented as highly sought after

relative to higher-skilled candidates.1

In the present study, we take a step

toward unifying these insights by con-

ducting a large-scale study of whether

the effect of status on perceptions of

quality is moderated by the underlying

quality of the performance itself. The

key limitation of current studies (see

Foschi and Valenzuela 2012; Lynn et al.

2016) is that the number of performance

levels under investigation has generally

been limited to just two (e.g., high and

low performers). The data we employ

here come from a large, experimental

market (Salganik 2008) and allow us to

test the interaction over a broad spec-

trum of quality levels. Specifically, we

test whether the effect of choice status

on perceived likeability varies by intrin-

sic appeal.

PREVIOUS RESEARCH

With regard to choice status, prior

research suggests that the effect of status

will be more pronounced for lower rather

than higher performers (see Foschi et al.

1995). The literature offers two rationales

for this hypothesis. First, to the extent

that positive herding is more likely than

1In addition, although not a comparativestudy of different quality levels, Kovacs and Shar-key (2014) find that for the very best books in themarket (i.e., nominees for a prestigious award),books that actually win the award do not experi-ence an increase in ratings; in fact, ratings tendto decline faster in the post-award period com-pared to books that were nominated but did notwin.

Is Popular More Likeable? 169



negative herding (Muchnik, Aral, and

Taylor 2013), popularity information is

more likely to boost rather than erode

favorability (for a given performance

level). Because of this asymmetrical

effect, and assuming that there is a theo-retical ceiling on what constitutes perfor-

mance excellence, this means that low

quality performers have more to gain

from status distortion than excellent per-

formers. For example, if a candidate’s

true skill or performance level is at the

85th percentile of the distribution, the

opportunity for status to upwardly distortis much smaller than if the candidate is at

the 45th percentile.

A second line of thinking posits that

evaluators are effectively more ‘‘moti-

vated’’ to incorporate status information

at lower levels of quality. The key idea

is that in the presence of ‘‘indisputable

evidence’’ establishing ‘‘merit,’’ evalua-tors no longer need to call upon the infor-

mation embedded in status characteris-

tics to help them form judgments (e.g.,

Benard and Correll 2010; Foschi and

Valenzuela 2012; Heilman et al. 1988).

That is, above a certain threshold of per-

formance ability, status bias will not be

activated, which is to say that no weightwill be given to status information in

the formation of quality perceptions.

Conversely, when performance informa-

tion is not available or is demonstrated

to be average or of lower quality, doubt

arises as to the candidate’s worthiness,

and status information is called in to

help adjudicate this doubt. In summary,both because (1) evaluators may be

more motivated to rely on status cues to

judge lower quality performances and

(2) there is ‘‘more room’’ for choice status

to elevate low versus high quality

performances, choice status cues should

ultimately play a greater role in boosting

perceptions of lower quality perfor-mances compared to higher quality

performances.

THE MUSICLAB EXPERIMENT

A decade ago, Salganik, Dodds, and Watts

(2006; see also Salganik and Watts 2008)

published what has become a landmark

experimental study on social influence.

The Musiclab project consisted of four

experiments that drew over 27,000 partic-

ipants in 2004 and 2005. On the website,

participants could listen to—then rate

and download—any of 48 songs, sampled

in any order. The key manipulation was

whether participants had access to popu-

larity information: those in the control

conditions were given only the names of

the songs and bands, whereas those in

the social influence conditions could see

the number of times each song had been

downloaded by previous participants. A

key finding from Salganik et al. (2006) is

that popularity information generated

greater market-level inequality in down-

load distribution—a result that clearly

implies that participants’ adoption

choices were, at least on average, posi-

tively influenced by what other partici-

pants before them had chosen.

The data have never been used, how-

ever, to examine the extent to which pop-

ularity actually affected private likeabil-

ity ratings. We do know from aggregate

measures (song-level or market-level sta-

tistics), that (1) popularity strongly pre-

dicted whether participants decided to lis-

ten to a song (Krumme et al. 2012;

Salganik et al. 2006; Salganik and Watts

2008) and (2) a participant’s private rat-

ing of a song’s likeability was strongly

associated with whether that song was

ultimately downloaded (Salganik and

Watts 2008). But the effect of popularity

on private ratings of likeability has not

been the subject of its own investigation.

Does a song’s choice status boost its like-

ability ratings, as Gould (2002) predicts?

Moreover, is this effect the same for songs

that are intrinsically ‘‘good’’ versus ‘‘bad’’?

In our empirical analyses, we analyze the

170 Social Psychology Quarterly 79(2)



data from Experiment 1 (weak popularity

signal) and Experiments 2 and 3 (strong

popularity signal) and find that popular-

ity can boost perceptions of likeability

much more for ‘‘bad’’ songs than ‘‘good’’

songs and that this interaction is not an

artifact of a mere statistical ceiling.

Experimental Design

Participants were given the opportunity

to download any number of 48 previously

unknown ‘‘indie rock’’ songs for free (Sal-

ganik 2008). Experiment 1 began in Octo-

ber 2004 and ran for 69 days, and Exper-

iment 2 ran for the 83 days immediately

following Experiment 1; participants for

Experiments 1 and 2 were recruited

mainly from a teen interest website.

Experiment 3, which recruited partici-

pants from a different population, began

in March 2005 and ran for 23 days (for

details, see Salganik 2008).2

Upon arrival, participants were ran-

domly assigned to one of two conditions:

independent versus social influence. In

Experiment 1, participants in the inde-

pendent (control) condition were given

a menu of songs formatted in a 16 3 3

rectangular grid (Salganik 2008: Figure

7). Songs were randomly assigned to 1 of

the 48 positions in that grid and identified

only by band name and song title. Those

assigned to the social influence condition

were further assigned to one of eight sep-

arate ‘‘worlds’’ (Salganik 2008: Figure 1).

In each of these eight worlds, respondents

were shown the names of the songs and

bands as well as the number of times

each song had been downloaded by others

arriving before them in their specific

world; all songs began with zero down-

loads and then accumulated downloads

in real time. Thus, because of this multi-

ple world design, each song has eight

‘‘lives’’ (i.e., we observe the counterfactual

eight times), which means that we can

isolate the effect of a song’s popularity

from its intrinsic characteristics.

The key difference between Experi-

ments 2 and 1 is the format in which the

48 songs were presented to the partici-

pants. In contrast to the 16 3 3 grid

used in Experiment 1, songs in Experi-

ments 2 and 3 were presented in a single

column (48 3 1) and rank ordered by the

number of times they were previously

downloaded, with the most popular song

listed at the top (Salganik 2008: Figure

8). Thus, because popularity is coupled

with a visibility advantage in Experiment

2, the popularity signal is stronger in

Experiment 2 than in 1. Experiment 3 is

identical to Experiment 2 except that

Experiment 3 draws from a different popu-

lation and contains only two social influ-

ence worlds instead of eight, which drasti-

cally reduces the amount of variability on

popularity per song.

In each experiment, decision making is

structured as a (one-way) three-step

sequence: listen�!rate�!download. Par-

ticipants were able to listen to any song

simply by clicking on it. While the song

was playing, participants were asked to

rate the song’s likeability on a five-star

scale. After submitting a rating, partici-

pants were given the opportunity to

download the song (yes or no). For our

purposes, our analysis examines all of

the song ratings that are available in

each experiment; Table 1 provides

descriptive statistics both at the person

and ratings level.

Measures

Song popularity. A song’s popularity is

defined as the number of times it was pre-

viously downloaded in the participant’s

2Experiment 4 is not analyzed here. Comparedto 1 through 3, Experiment 4 contains a layer ofcomplexity that complicates the interpretationof findings regarding the interaction of interest.




world. Because downloads accumulate in

real time, the download distribution

seen by the participant is contingentupon time of arrival to the site. For exam-

ple, the 5th person to join a world, by def-

inition, will see counts that range (at

most) from 0 to 4, whereas the 85th per-

son to participate could potentially see

downloads that range from 0 to 84. Thus,

for robustness purposes, we model popu-

larity two different ways: as raw downloadcounts as well as counts converted into z-

scores. This transformation preserves the

relative standing of songs but accounts

for differences in the average number of

downloads and the spread in downloads.

For example, for the 85th person, the dif-

ference between 4 downloads might seem

trivial whereas for the 5th person, a leadof 4 might seem substantial.

Perceived likeability. When listening to

a song, participants were asked to rate

its likeability using a five-star rating

scheme: I love it = 5, I like it = 4, It’s

OK = 3, I don’t like it = 2, I hate it = 1.The distribution of stars was approxi-

mately normal in each experiment. See

Table 1 for a descriptive summary of the

distribution by experiment.

Intrinsic appeal. To measure a song’s

‘‘true’’ or intrinsic appeal, we calculate

the average rating for each song from

the corresponding control condition (i.e.,

the one condition in each experiment

without popularity information).3 The

distribution of intrinsic appeal for the 48

songs varied slightly across experiments:

Experiment 1 (M = 2.9, SD = .40), Exper-

iment 2 (M = 3.2, SD = .38), Experiment 3(M = 2.5, SD = .37). Experiments 1 and 2

are drawn from the same population, and

not surprisingly, ‘‘natural’’ tastes are

highly correlated (r12 = .86). Experiment 3

is drawn from a different population, and

its tastes are not as strongly correlated

with the first two (r13 = .78; r23 = .53).

Decision order. Respondents may

change their standards for likeability

over the course of rating many songs.

With the Musiclab data, we know the

order in which songs were selected for lis-

tening and thus the order that songs were

rated. See Table 1 for a descriptive sum-

mary of the number of songs rated in

each experiment. A large proportion of

Table 1. Descriptive Statistics

Experiment 1 Experiment 2 Experiment 3

M SD M SD M SD

RatingsPrivate likeability rating (5-star scale) 3.0 1.2 3.3 1.2 2.7 1.0Mean number of downloads at time of rating 7.9 5.3 10.3 6.2 11.0 6.3N 19,954 18,229 10,131

RatersNumber of listens 7.2 9.7 6.8 9.4 10.5 10.9Number of ratings 6.8 9.2 6.2 8.7 9.8 10.2Number of downloads 2.2 4.5 2.7 5.8 2.0 4.9Percentage male (1 = yes, 0 = no) 65 26 63Age 22.4 9.8 19.3 7.4 30.8 11.6Percentage ever downloadedmusic online (1 = yes, 0 = no)

63 64 75

N 2,951 2,953 1,035

3See Salganik, Dodds, and Watts 2006 as wellas Salganik and Watts 2008 for an alternativeformulation of intrinsic appeal.




raters in each experiment rated ten or

more songs (58 percent, 57 percent, and

68 percent in Experiments 1, 2, and 3,

respectively).

RESULTS

The Musiclab data are well suited to test

for an interaction between popularity

and intrinsic appeal given the extent of

variation observed for both intrinsic

appeal as well as popularity. The dotplots

in Figure 1 illustrate this for Experiment

1. Each vertical cluster corresponds to

a song, anchored at its level of intrinsic

appeal (x-axis). Each dot represents

a respondent, and the y-axis is the level

of popularity attached to the song when

the respondent listened to it. Figure 1

clearly shows that (1) intrinsically more

appealing songs are likelier to become

popular (Salganik et al. 2006; Salganik

and Watts 2008) but that (2) regardless

of intrinsic appeal, each song is seen by

respondents at a variety of popularity lev-

els. These data are thus akin to an exper-

iment with 48 levels of true quality

‘‘crossed’’ with a substantial range of pop-

ularity levels.

The key empirical questions are

whether the popularity of a song affects

ratings of its likeability and whether

this effect varies by the level of intrinsic

quality of that song. We begin with

a series of fixed effects ordinary least

squares (OLS) regression models to pre-

dict the number of stars a song was rated

(i.e., private perceptions of likeability) as

a function of a song popularity, the song’s

intrinsic appeal, the interaction between

popularity and appeal, person fixed

effects, and decision order.4 Clustered

standard errors are used to account for

the fact that ratings are nested with 48

songs. A person fixed effects approach

means that we are controlling for all

time-constant sources of person-level

variation and thus estimate the effect of

popularity on song ratings by leveragingwithin-person variation in song ratings.

Table 2 summarizes the results for

Experiment 1 with popularity measured

as raw download counts (divided by ten)

in M1 and z-scores in M2. The results

indicate that (1) intrinsic appeal has

a major effect on likeability perceptions,

which is not surprising, and (2) the

extent to which popularity can boost like-

ability is substantially greater for lower

quality songs. For example, with respect

to M1, the effect of popularity on likeabil-

ity for a ‘‘true’’ four-star song is esti-mated to be .423 – (4 3 .096) = .039

(i.e., adding ten more downloads is

expected to generate roughly a zero-star

increase in likeability). However, for

a song with two-star intrinsic appeal,

the effect of popularity is meaningfully

larger: .349 – (1 3 .078) = .23, which cor-

responds to nearly a quarter star bonusin perceived likeability for ten additional

downloads. Note that the same interac-

tion is detected when popularity is

measured with respect to z-scores of

download counts, which hones in on rela-

tive popularity (M2). This pattern of

results is virtually the same for Experi-

ments 2 and 3 (M3 and M4, respectively).In sum, even when the popularity cue is

more visible, the extent to which

popularity boosts likeability is still con-

strained by the level of intrinsic appeal.

A question that arises is whether this

interaction effect is an artifact of a statis-

tical ceiling. That is, in contrast to the

theoretical ceiling discussed earlier, per-

haps there is no effect of popularity on

ratings for ‘‘good’’ songs simply because

the ordinal rating scale is capped at five

stars (‘‘I love it.’’) and thus there is liter-

ally no room for likeability to increase.

4The ordinary least squares results are sub-stantively the same as those from an ordinallogistic regression as well as a logistic regressionpredicting the probability of receiving five starsversus fewer than five stars.




We argue that this is unlikely the case

here, given that there is still plenty of

room for ratings to increase even for the

very best songs. For example, for the

song with the highest intrinsic appeal in

Experiment 1 (song 25, Parker Theory,

‘‘She Said’’), the proportion of five-star

ratings received was still only 23 percent,

020

4060

80

1.5 2 2.5 3 3.5 4

-4-2

02

46

1.5 2 2.5 3 3.5 4

Intrinsic Appeal

z-score

Number of Downloads

Figure 1. Distribution Plot of Popularity Levels Over Intrinsic Appeal, Experiment 1




with a full 40 percent of raters giving it

three or fewer stars.

To further investigate the nature of the

interaction, we estimated, per experi-

ment, 48 separate regressions (one for

each song) to isolate the effect of popular-

ity ‘‘within’’ each song. Table 3 illustrates

this approach for three songs in Experi-

ment 1. For example, in the control condi-

tion of Experiment 1, the song ‘‘Fear’’ by

the band Forthfading was rated a 3.64

on average (the second most ‘‘naturally’’

appealing song in this market). In the

eight social influence worlds, 501

respondents rated ‘‘Fear’’ and gave the

song a wide range of ratings (see Table 3

part A). We then regressed these 501 rat-

ings on rater characteristics (sex, age, and

whether the respondent had ever down-

loaded songs online) as well as the contex-

tual factors surrounding the decision,

including the popularity level (z-score) at

which ‘‘Fear’’ was seen, where ‘‘Fear’’

fell in the decision order of ratings, and

the mean number of downloads seen bythe respondent at the time that ‘‘Fear’’

was rated.5 For the song ‘‘Fear,’’ its popu-

larity level had no positive or negative

effect on likeability perceptions on aver-

age (b = .006, SE = .034). In contrast, forthe other two songs in Table 3, both of

which had lower intrinsic appeal com-

pared to ‘‘Fear,’’ popularity had a statisti-

cally and practically significant effect on

likeability perceptions.

To see how this pattern generalizes

across all songs and within each experi-

ment, Figure 2 summarizes the point esti-

mate for popularity from each of the 48 3

3 song-specific regressions. Circles and

error bars in Figure 2 correspond,

Table 2. Ordinary Least Squares Estimates Predicting Likeability Ratings

M1 M2 M3 M4Experiment 1 1 2 3Measure of popularity Downloadsa z-score z-score z-score

Popularity .42*** .31*** .35*** .29***(.11) (.06) (.09) (.08)

Intrinsic appeal .82*** .72*** .74*** .85***(.04) (.04) (.04) (.04)

Popularity 3 intrinsic appeal –.10** –.07*** –.08** –.09**(.03) (.02) (.02) (.03)

Decision order –.00* –.00 .01*** .00(.00) (.00) (.00) (.00)

Person fixed effects Yes Yes Yes YesConstant .49*** .88*** .69*** .37***

(.10) (.10) (.14) (.09)Number of raters 2,951 2,951 2,953 1,035Observations 19,954 19,954 18,229 10,131Adjusted R2 (percent) 40.2 40.5 42.1 35.8

aNumber of downloads (divided by 10).*p \ .05. **p \ .01. ***p \ .001.

5To illustrate, person 1132, a middle agedmale in world 4 (Experiment 1), gave ‘‘Fear’’three stars. It was the fourth song he listenedto, and ‘‘Fear’’ had zero downloads at the time(z-score = –1.4). The mean number of downloadswhen 1132 came to the market was only 1.8. Incontrast, person 6705, a teenage female in world1, saw ‘‘Fear’’ as being downloaded 42 times pre-viously (3.0 standard deviations above the meannumber of downloads, which was roughly 13).‘‘Fear’’ was the fourteenth song that she listenedto and she gave it four stars.




respectively, to the point estimate and 95

percent confidence interval for the effect

of popularity ‘‘within’’ each song (e.g., if

y = 2, this means that for a 1 standard

deviation increase in popularity, there is

a corresponding 2-star increase on aver-

age in likeability for that particular

song). We see that in each experiment,

the effect of popularity hovers around

zero for the top third of the most intrinsi-

cally appealing songs. Only for songs of

lower and average intrinsic appeal do we

see cases in which popularity produced

a likeability halo.6

DISCUSSION

The intuitive and scholarly expectation is

that an object or actor’s choice status will

positively affect perception of its quality

(e.g., Gould 2002; Lynn et al. 2016).

Table 3. Illustration with Three Songs, Experiment 1

A. Descriptives

Song title and band ‘‘Fear’’by Forthfading

‘‘Bolshevik Boogie’’by Sum Rana

‘‘Miseries & Miracles’’by A Blinding Silence

Intrinsic appeala 3.64 stars(above average)

3.01 stars(average)

2.64 stars(below average)

Likeability ratingsb (percentages)One star 5 5 11Two stars 14 28 32Three stars 26 35 27Four stars 31 26 19Five stars 24 5 11

B. Ordinary least squaresestimates predictinglikeability ratings

b(SE)

b(SE)

b(SE)

Popularity .01 .34** .49**(.04) (.13) (.15)

Decision order .01 .01** –.01(.01) (.01) (.01)

Mean number of downloads .02 .01 .01(.01) (.01) (.01)

Age –.02*** .00 .00(.01) (.01) (.01)

Male .12 .14 –.30*(.11) (.11) (.12)

Ever downloaded music online –.19 .12 .12(.10) (.11) (.12)

Constant 3.82*** 2.64*** 3.02***(.20) (.20) (.23)

Number of raters 501 365 349R2 (percent) 4.3 4.8 6.6

aMean likeability rating in the control condition of experiment 1.bTabulated for all eight social influence worlds.*p \ .05. **p \ .01. ***p \ .001.

6See online appendix available at spq.sage-pub.com/supplemental for additional analyses.




Figure 2. Coefficient for Popularity with 95 Percent Confidence Interval for Individual Songs




Here, we document an important contin-

gency to this relationship, which is that

the effect of popularity appears to be lim-

ited to objects of lower and average qual-

ity. Indeed, in this context, there appears

to be no meaningful status advantage

with respect to private perceptions of like-

ability at higher levels of quality: for

songs that are intrinsically appealing,

being popular (unpopular) does not boost

(erode) perceptions of likeability. In con-

trast, popularity has the potential to

upwardly distort the likeability of a song

in the case of average and below average

songs.

There are two major implications of

our finding. First, researchers studying

the Matthew Effect should take note

that the mechanisms through which suc-

cess breeds success may not be one size

fits all. The basic assumption is that qual-

ity perceptions are formed as a weighted

combination of actual quality and the

extent to which the object is sought after

by others (Gould 2002). Here, however,

we find no evidence of this for objects

above a certain quality threshold. The

success of ‘‘good’’ songs was not fueled

by the fact that popularity boosted private

perceptions of likeability. Instead, ‘‘good’’

songs were more likely to become super-

stars in the market (see Figure 1) because(1) popularity cues heavily affected sam-

pling (Krumme et al. 2012; see also Den-

rell and Le Mens 2007) and (2) likeable

songs were more likely to be downloaded

(Salganik and Watts 2008). In other

words, popularity information was crucial

to the creation of superstars but not

through upward pressure on perceivedlikeability.

In contrast, it is the case that social

influence on perceptions of likeability

was able to elevate the success of songs

of lower intrinsic appeal in the market.

In other words, taking stock of what the

crowd is doing can allow poorer quality

performers to thrive in a way that is not

possible when the crowd plays no role in

decision making. This suggests a specific

type of compensatory process with respect

to choice status and perceptions of like-

ability, where a song can ‘‘survive’’ a low

quality performance by somehow attract-

ing the attention of many others. An

empirical issue that needs further clarifi-

cation, however, is whether choice status

matters differently for poor versus aver-

age performances; Figure 2 and the online

appendix tentatively suggest that the

effect of popularity is greater for average

compared to low performances, but the evi-

dence on this point is far from conclusive.

Above all, future research is clearly

needed to test whether this interaction

exists in other empirical settings and

with other kinds of status markers.7

Although the interaction effect is robust

in the Musiclab data and the data source

itself is compelling, the market is specific

to cultural goods and quality itself is not

difficult to ascertain.8 But deciding

whether a song is likeable and worthy of

downloading (especially in a cost-free

market) is very different than decidingwhether, say, a potential employee is

skillful and worthy of hire. To this point,

we suggest, for example, a simple exten-

sion of previous resume studies (Foschi

and Valenzuela 2012; Moss-Racusin

7In this vein, another question to consider isthe extent to which this moderation effect canor cannot be explained by the ‘‘inconsistencyeffect’’ discussed in the expectation states tradi-tion (see Berger et al. 1977).

8In addition, the Musiclab is based on a nesteddecision structure, which means that the songsthat participants rate have not been randomlyassigned but rather are songs that the participantfirst decided to listen to (i.e., an evaluation condi-tional on selection). In contrast, participants intraditional status experiments typically do nothave a choice as to which candidate they wouldlike to rate. Future studies should explore howthis difference in research design affects evalua-tion patterns with respect to different statusmarkers.




et al. 2012) where resumes are distrib-

uted over a broad range of performance

levels. We suspect that that the modera-

tion effect investigated here may be dimin-

ished in market settings characterized by

greater uncertainty around candidatequality (see Benard and Correll 2010).

SUPPLEMENTAL MATERIAL

Additional supporting information may be foundat spq.sagepub.com/supplemental.

ACKNOWLEDGMENTS

We are indebted to Rebecca Durkee, Sarah Hark-ness, Matt Salganik, Michael Sauder, BrentSimpson, Ezra Zuckerman, and the participantsof Iowa’s Theory Workshop for their feedback atvarious stages of this project. A portion of thispaper was presented at the annual Group Pro-cesses conference in 2014.

REFERENCES

Becker, Gary S. 1991. ‘‘A Note on RestaurantPricing and Other Examples of Social Influ-ences on Price.’’ Journal of Political Econ-omy 99:1109–16.

Benard, Stephen, and Shelley Correll. 2010.‘‘Normative Discrimination and the Moth-erhood Penalty.’’ Gender and Society24:616–46.

Berger, Joseph, M. Hamit Fisek, Robert Z.Norman, and Morris Zelditch. 1977. StatusCharacteristics and Social Interaction. NewYork: Elsevier.

Cialdini, Robert B. 1984. Influence. New York:William Morrow and Company.

Correll, Shelley J., Stephen Benard, and InPaik. 2007. ‘‘Getting a Job: Is There a Moth-erhood Penalty?’’ American Journal of Soci-ology 112:1297–338.

Denrell, Jerker, and Gael Le Mens. 2007.‘‘Interdependent Sampling and SocialInfluence.’’ Psychological Review 114:398–422.

DiPrete, Thomas A., and Gregory M. Eirich.2006. ‘‘Cumulative Advantage as a Mecha-nism for Inequality: A Review of Theoreti-cal and Empirical Developments.’’ AnnualReview of Sociology 32:271–97.

Foschi, Martha, Kirsten Sigerson, and MarieLembesis. 1995. ‘‘Assessing Job Applicants:The Relative Effects of Gender, Academic

Record, and Decision Type.’’ Small GroupResearch 26:328–52.

Foschi, Martha, and Jerilee Valenzuela. 2012.‘‘Who Is the Better Applicant? Effects fromGender, Academic Record, and Type of Deci-sion.’’ Social Science Research 41:949–64.

Gould, Roger V. 2002. ‘‘The Origins of StatusHierarchies: A Formal Theory and Empiri-cal Test.’’ American Journal of Sociology107:1143–78.

Hedstrom, Peter. 1998. ‘‘Rational Imitation.’’Pp. 306–27 in Social Mechanisms: An Ana-lytical Approach to Social Theory, edited byP. Hedstrom and R. Swedberg. Cambridge:Cambridge University Press.

Heilman, Madeline E., Richard F. Martell, andMichael C. Simon. 1988. ‘‘The Vagaries ofSex Bias: Conditions Regulating the Under-valuation, Equivaluation, and Overvalua-tion of Female Job Applicants.’’ Organiza-tional Behavior and Human DecisionProcesses 41:98–110.

Kovacs, Balazs, and Amanda J. Sharkey. 2014.‘‘The Paradox of Publicity How Awards CanNegatively Affect the Evaluation of Qual-ity.’’ Administrative Science Quarterly59:1–33.

Krumme, Coco, Manuel Cebrian, Galen Pick-ard, and Sandy Pentland. 2012. ‘‘Quantify-ing Social Influence in an Online CulturalMarket.’’ PLoS ONE 7:e33785.

Lynn, Freda B., Joel Podolny, and Lin Tao.2009. ‘‘A Sociological (De)Construction ofthe Relationship between Status and Qual-ity.’’ American Journal of Sociology115:755–804.

Lynn, Freda B., Brent Simpson, Mark H.Walker, and Colin Peterson. 2016. ‘‘Why isthe Pack Persuasive? The Effect of ChoiceStatus on Perceptions of Quality.’’ Sociolog-ical Science 3: 239–263.

Manzo, Gianluca, and Delia Baldassarri. 2015.‘‘Heuristics, Interactions, and Status Hier-archies An Agent-Based Model of DeferenceExchange.’’ Sociological Methods &Research 44(2):329–87.

Merton, Robert K. 1968. ‘‘Matthew Effect inScience.’’ Science 159:56–63.

Moss-Racusin, Corinne A., John F. Dovidio,Victoria L. Brescoll, Mark J. Graham, andJo Handelsman. 2012. ‘‘Science Faculty’sSubtle Gender Biases Favor Male Stu-dents.’’ Proceedings of the National Acad-emy of Sciences 109:16474–79.

Muchnik, Lev, Sinan Aral, and Sean J. Taylor.2013. ‘‘Social Influence Bias: A RandomizedExperiment.’’ Science 341:647–51.




Podolny, Joel M. 1993. ‘‘A Status-Based Modelof Market Competition.’’ American Journalof Sociology 98:829–72.

Salganik, Matthew J. 2008. Data from Successand Failure in Cultural Markets. RetrievedApril 11, 2016 (http://opr.princeton.edu/archive/cm/data_release.pdf).

Salganik, Matthew J., Peter Sheridan Dodds,and Duncan J. Watts. 2006. ‘‘ExperimentalStudy of Inequality and Unpredictability inan Artificial Cultural Market.’’ Science311:854–55.

Salganik, Matthew J., and Duncan J. Watts.2008. ‘‘Leading the Herd Astray: An Exper-imental Study of Self-Fulfilling Propheciesin an Artificial Cultural Market.’’ SocialPsychology Quarterly 71:338–55.

BIOS

Freda B. Lynn is an associate professor

of sociology at the University of Iowa.

Her research interests include inequality,status dynamics, and networks. In her

current work, she explores individual-level

perceptions as the building blocks of

hierarchical structures. Some of her

previous research on status has been

funded by the National Science Founda-

tion and appears in the American Jour-

nal of Sociology and the Annual Reviewof Sociology.

Mark H. Walker is an assistant profes-

sor of sociology at Louisiana State Uni-

versity. His research focuses primarily

on identity processes, social networks,

and mental health outcomes. His

research has appeared in Social Psychol-ogy Quarterly, Society and Mental

Health, and Sociological Science.

Colin Peterson is a graduate student in

the Department of Sociology at Stanford

University. His research interests include

inequality, social psychology, networks,

and computational social science.




is popular more likeable? choice status by intrinsic

Documents